Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study.
Hideki KawaiSadako MotoyamaMasayoshi SaraiYoshihiro SatoTakahiro MatsuyamaRyota MatsumotoHiroshi TakahashiAkio KatagataYumi KataokaYoshihiro IdaTakashi MuramatsuYoshiharu OhnoYukio OzakiHiroshi ToyamaJagat NarulaHideo IzawaPublished in: European radiology (2023)
• Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging. • Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution. • Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting.
Keyphrases
- deep learning
- coronary artery
- computed tomography
- dual energy
- pulmonary artery
- positron emission tomography
- image quality
- artificial intelligence
- convolutional neural network
- contrast enhanced
- machine learning
- magnetic resonance imaging
- coronary artery disease
- antiplatelet therapy
- label free
- real time pcr
- optic nerve
- percutaneous coronary intervention
- acute coronary syndrome